Global 3D Non-Rigid Registration of Deformable Objects Using a Single RGB-D Camera

Yang, Jingyu; Guo, Daoliang; Li, Kun; Wu, Zhenchao; Lai, Yuekun

doi:10.1109/tip.2019.2909197

Cited by 12 publications

(11 citation statements)

References 50 publications

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“…The data from the RGB-D camera and 2D lidar are subjected to the corresponding perception algorithm to obtain the result and then aggregated and fused with the estimation results of the encoder and IMU [31]. Then the results obtained after the aggregation and fusion are transmitted to the loop closure detection and graph optimization module [32]. Finally, the system framework outputs OctoMap, point cloud, 2D occupancy raster map, map data, Map Graph and TF [33].…”

Section: ) Four Types Of Sensor Fusion Slam Frameworkmentioning

confidence: 99%

Research on Multi-Sensor Fusion SLAM Algorithm Based on Improved Gmapping

Tian

Liu

et al. 2023

IEEE Access

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Simultaneous Localization and Mapping (SLAM) is the core technology of the intelligent robot system, and it is also the basis for its autonomous movement. In recent years, it has been found that SLAM using a single sensor has certain limitations, such as Inertial Measurement Unit (IMU) noise and serious drift, and 2D radar can only detect environmental information on the same horizontal plane. In this regard, this paper constructs a multi-sensor back-end fusion SLAM algorithm that combines vision, laser, encoder and IMU information. Experiments have proved that compared with using a single sensor, the application of a multi-sensor fusion system makes the edges of the constructed map clearer and the noise reduced. Aiming at the problem of increased calculation caused by particle degradation and too many particles, this paper improves the Gmappping algorithm, and uses the combination of selective resampling and Kullback-Leibler Distance (KLD) sampling to complete resampling. It has been proved by experiments that compared with the original algorithm of Gmapping, the application of the improved algorithm increases the particle convergence speed by 39.85% in the process of indoor mapping. Aiming at the problems that the traditional loop detection algorithm is easily affected by environmental factors, resulting in low detection accuracy, and the loop detection algorithm based on deep convolutional neural network has a large amount of calculation and takes a long time to detect. The main research of this paper is to apply a deep learning-based loop detection algorithm on the multi-sensor fusion framework, and use the combination of high-dimensional and low-dimensional features of the image for loop detection. This paper uses different algorithms to conduct comparative experiments on the dataset CityCentre. The experimental results show that compared with the traditional algorithms Bag of Words (BoW), AlexNet algorithm, VGG19 algorithm, and ResNet32 algorithm, the accuracy of the algorithm proposed in this paper has increased by 31.26%, 14.21%, 3.05%, and 1.56%, respectively. In addition, the comparison experiment results of SLAM mapping with the original Real-Time Appearance-Based Mapping (RTAB-MAP) algorithm prove that the loop closure detection algorithm based on deep learning proposed in this paper can enable the system to better build a globally consistent map, including more environmental information.

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Section: ) Four Types Of Sensor Fusion Slam Frameworkmentioning

confidence: 99%

Research on Multi-Sensor Fusion SLAM Algorithm Based on Improved Gmapping

Tian

Liu

et al. 2023

IEEE Access

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“…In addition, various geometric constraint terms have been introduced to preserve the local shape of the source model during deformation. Examples include preservation of Laplacian operators [20], [21], [22], as-conformal-as-possible deformations [23], and as-rigid-aspossible deformations [24].…”

Section: Related Workmentioning

confidence: 99%

Fast and Robust Non-Rigid Registration Using Accelerated Majorization-Minimization

Yao

Deng

et al. 2023

IEEE Trans. Pattern Anal. Mach. Intell.

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Non-rigid 3D registration, which deforms a source 3D shape in a non-rigid way to align with a target 3D shape, is a classical problem in computer vision. Such problems can be challenging because of imperfect data (noise, outliers and partial overlap) and high degrees of freedom. Existing methods typically adopt the ℓp type robust norm to measure the alignment error and regularize the smoothness of deformation, and use a proximal algorithm to solve the resulting non-smooth optimization problem. However, the slow convergence of such algorithms limits their wide applications. In this paper, we propose a formulation for robust non-rigid registration based on a globally smooth robust norm for alignment and regularization, which can effectively handle outliers and partial overlaps. The problem is solved using the majorization-minimization algorithm, which reduces each iteration to a convex quadratic problem with a closed-form solution. We further apply Anderson acceleration to speed up the convergence of the solver, enabling the solver to run efficiently on devices with limited compute capability. Extensive experiments demonstrate the effectiveness of our method for non-rigid alignment between two shapes with outliers and partial overlaps, with quantitative evaluation showing that it outperforms state-of-the-art methods in terms of registration accuracy and computational speed. The source code is available at https://github.com/yaoyx689/AMM NRR.

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“…[63] proposed a template-less nonrigid reconstruction method with a single RGB-D camera, using an efficient local-to-global hierarchical optimization framework. [64] removes the need for template prior to compute deformations by optimizing a global alignment problem and use an as-rigid-as-possible constraint to eliminate the shrinkage problem of the deformed shapes, especially near open boundaries of scans.…”

Section: E Non-rigid Reconstructionsmentioning

confidence: 99%

Survey and Evaluation of RGB-D SLAM

2021

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The traditional visual SLAM systems take the monocular or stereo camera as input sensor, with complex map initialization and map point triangulation steps needed for 3D map reconstruction, which are easy to fail, computationally complex and can cause noisy measurements. The emergence of RGB-D camera which provides RGB image together with depth information breaks this situation. While a number of RGB-D SLAM systems have been proposed in recent years, the current classification research on RGB-D SLAM is very lacking, and their advantages and shortcomings remain unclear regarding different applications and perturbations, such as illumination transformation, noise and rolling shutter effect of sensors. In this paper, we mainly introduced the basic concept and structure of the RGB-D SLAM system, and then introduced the differences between the various RGB-D SLAM systems in the three aspects of tracking, mapping, and loop detection, and we make a classification study on different RGB-D SLAM algorithms according to the three aspect. Furthermore, we discuss some advanced topics and open problems of RGB-D SLAM, hoping that it will help for future exploring. In the end, we conducted a large number of evaluation experiments on multiple RGB-D SLAM systems, and analyzed their advantages and disadvantages, as well as performance differences in different application scenarios, and provided references for researchers and developers.

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Global 3D Non-Rigid Registration of Deformable Objects Using a Single RGB-D Camera

Cited by 12 publications

References 50 publications

Research on Multi-Sensor Fusion SLAM Algorithm Based on Improved Gmapping

Research on Multi-Sensor Fusion SLAM Algorithm Based on Improved Gmapping

Fast and Robust Non-Rigid Registration Using Accelerated Majorization-Minimization

Survey and Evaluation of RGB-D SLAM

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